1. Field of the Invention
The present invention relates to a method and system for creating and using data mining recommendation models that do not rely on having specific histories of activities, but which can establish relationships among data at a higher level of abstraction to create such models.
2. Description of the Related Art
The ability to generate quality recommendations for customers has been approached by numerous companies with a variety of approaches. However, these approaches generally assume that the products being recommended have a purchase or use history and a future, that is, they can be recommended for purchase or use in the future. Where products do not have a history, it is expected that proxies can be assigned to them until each product has its own history. Conventional approaches to recommendation systems assume that historical information is available or that those proxies are similar enough so that they can be used effectively as the basis of a predictive system. In cases where those conditions are not met the use of data mining and recommendations is much more limited or not easily applicable.
Similar challenges are encountered in other problems where the products are single use, for example, conference sessions. Conference sessions are normally available exactly once. The problem is how to recommend such sessions where there is no past history of their attendance, and no future instances of their delivery. The type of information that is available for such a problem involves, for example, past years' session, attendee, and session attendance data. However, this data is specific for sessions that were basically unique and do not repeat themselves from one year to the next. Similarly on the attendee's side, many people are new attendees every year and may not be well represented by other past attendees.
The crux of the problem is then how to leverage previous years' data where there is no direct proxy mapping for either sessions or attendees to generate session recommendations. In the more general case, the problem is how to make product recommendation models that are more general and represent relationships at a more abstract level that do not rely on having specific histories for the actual product or the customers, but can leverage past instances representing the acquisition, rating or attendance of other products by other customers.